aeo-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| audit_urlA | Run a full AI-readiness (AEO) audit of a website: 29 checks across AI crawler access, structured data, content extractability, and answerability, returning a 0-100 score with prioritized fixes. Use when asked how visible a site is to ChatGPT, Perplexity, Gemini, or Claude, or for an overall AEO/AI-SEO health check. |
| check_ai_crawlersA | Check whether AI crawlers like GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and Bingbot are allowed to read a website, based on its robots.txt. Use when asked about AI visibility, AEO, crawler permissions, or why a site does not appear in ChatGPT or Perplexity. |
| inspect_llms_txtA | Fetch and validate a website's llms.txt file (the Markdown file that tells AI engines what the site is about and which pages matter). Returns its title, summary, sections, links, and any structural issues. Use when asked whether a site has llms.txt or how to improve it. |
| extract_structured_dataA | Extract and summarize a page's JSON-LD structured data (schema.org): which types exist (Product, Article, FAQPage, Organization), their key fields, and any parse errors. Use when asked whether a page has the schema markup AI answer engines rely on for citations. |
| extract_page_signalsA | Extract the on-page signals AI engines parse: title, meta description, h1, canonical, lang, text-to-HTML ratio, heading outline, and whether the page looks client-side rendered (invisible to most AI crawlers). Use to diagnose why engines misread or skip a page. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a distinct aspect of AI readiness: overall audit, crawler permissions, llms.txt, structured data, and page signals. No overlap in purpose.
All tool names follow a consistent verb_noun pattern with underscores, e.g., audit_url, check_ai_crawlers, inspect_llms_txt, extract_structured_data, extract_page_signals.
Five tools is well-scoped for the focused domain of AI-readiness auditing. Each tool covers a key area without being overwhelming or too sparse.
The set covers core AI-readiness checks (crawlers, llms.txt, structured data, on-page signals) and a comprehensive audit. Minor gaps like content quality or answerability simulation are likely integrated into the audit tool.